Biblio
In cognitive radio networks with mobile terminals, it is not enough for spectrum sensing only to determine whether primary user (PU) occupy the spectrum band. Sometimes we also want to know more priori information, such as, the number of PUs, which can help to estimate its carrier frequency, direction of arrival, and location. In this paper, a machine learning based method is proposed to estimate a large number of primary users. In the proposed method, support vector machine (SVM) is used to achieve the number of primary users while genetic algorithm (GA) is to optimize the parameters of SVM kernel. The first class feature of SVM is the ratio of the element sum and the trace of sample covariance matrix, and the second class feature is the mean of Gerschgorin radii. The simulation results show that our proposed SVM-GA algorithm has higher accuracy than SVM.
Genetic Algorithms are group of mathematical models in computational science by exciting evolution in AI techniques nowadays. These algorithms preserve critical information by applying data structure with simple chromosome recombination operators by encoding solution to a specific problem. Genetic algorithms they are optimizer, in which range of problems applied to it are quite broad. Genetic Algorithms with its global search includes basic principles like selection, crossover and mutation. Data structures, algorithms and human brain inspiration are found for classification of data and for learning which works using Neural Networks. Artificial Intelligence (AI) it is a field, where so many tasks performed naturally by a human. When AI conventional methods are used in a computer it was proved as a complicated task. Applying Neural Networks techniques will create an internal structure of rules by which a program can learn by examples, to classify different inputs than mining techniques. This paper proposes a phishing websites classifier using improved polynomial neural networks in genetic algorithm.